ESTRO 2025 - Abstract Book

S3727

Physics - Radiomics, functional and biological imaging and outcome prediction

ESTRO 2025

Conclusion: The dosiomics model integrating the interaction between CT and BED can effectively predict treatment failure following lung SBRT treatment and may serve as a useful tool to proactively evaluate and select lung SBRT treatment plans to reduce treatment failure in the future.

Keywords: Lung SBRT, Dosiomics, Treatment failure

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Mini-Oral Histological validation of time-dependent diffusion MRI in glioblastoma for biologically adapted radiotherapy Minea Jokivuolle 1,2 , Henrik Lundell 3,4 , Kristoffer H Madsen 3,5 , Frantz R Poulsen 2,6,7 , Jeanette K Petersen 8 , Mads H Grønhøj 2,6,7 , Frederik SG Harbo 9 , Jesper D Ewald 2,8 , Anne LH Bisgaard 1,2 , Faisal Mahmood 1,2 1 Laboratory of Radiation Physics, Department of Oncology, Odense University Hospital, Odense, Denmark. 2 Department of Clinical Research, University of Southern Denmark, Odense, Denmark. 3 Danish Research Centre for Magnetic Resonance, Department of Radiology and Nuclear Medicine, Copenhagen University Hospital - Amager and Hvidovre, Hvidovre, Denmark. 4 Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark. 5 Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark. 6 Department of Neurosurgery, Odense University Hospital, Odense, Denmark. 7 BRIDGE (Brain Research Interdisciplinary Guided Excellence), University of Southern Denmark, Odense, Denmark. 8 Department of Pathology, Odense University Hospital, Odense, Denmark. 9 Department of Radiology, Odense University Hospital, Odense, Denmark Purpose/Objective: In radiation therapy (RT), the apparent diffusion coefficient (ADC) derived from diffusion-weighted MRI (DW-MRI) is increasingly used for tumor microstructure characterisation, paving the way for individualized RT. [1] ADC reflects the degree of diffusion restriction in tissue and has been linked to cell density but can be confounded by diffusional exchange. [2] Time-dependent diffusion MRI (TDD-MRI), utilizes time-dependent differences in the DW-MRI signal to detect both restricted diffusion and diffusional exchange, [2] potentially providing less confounded measures of tumor microstructure. We recently investigated time-dependent diffusion effects in brain lesions on clinical MRI systems using time-dependent diffusion (TDD) contrast . [3] The current study aims to validate the TDD contrast with histology in a small cohort. Material/Methods: Seven patients with glioblastoma, IDH-wildtype, CNS WHO grade 4, were pre-operatively scanned with TDD-MRI on a 1.5 T MRI system (Ingenia, Philips HealthCare). During gross-tumor resection, 2-3 biopsies with known locations in the pre-operative MRI were obtained from each patient and processed to produce hematoxylin and eosin (HE) stained sections. Cell density was estimated by automatic nuclei detection using a threshold-based classifier (Visiopharm) on scanned histopathological slides. The TDD contrast maps were calculated as a subtraction of normalized DW-MRI signal acquired with same b-value ( b = 2500 s/mm 2 ) but two different effective diffusion times (26 ms and 44 ms). [3] ADC maps were calculated as a voxel-wise mono-exponential fit to the DW-MRI signal. Association between cell density and TDD contrast was determined using Spearman’s rank correlation coefficient ( ρ s ) with a significance level of p < 0.05. As a post hoc analysis, the association between cell density and ADC was also determined. Results: Of the 18 obtained biopsies, 12 were determined by a neuropathologist to contain viable tumor tissue and were used for cell density analysis. Figure 1 shows two examples of biopsy locations with high and low cell density. The TDD contrast correlated with cell density ( ρ s = 0.61, p = 0.04), whereas the ADC did not ( ρ s = -0.25, p = 0.4) in this small sample (Figure 2).